دورية أكاديمية
Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
| العنوان: | Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net |
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| المؤلفون: | Andrew Clark, Joel McKechnie |
| المصدر: | Applied Sciences, Vol 10, Iss 6, p 2017 (2020) |
| بيانات النشر: | MDPI AG, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Technology LCC:Engineering (General). Civil engineering (General) LCC:Biology (General) LCC:Physics LCC:Chemistry |
| مصطلحات موضوعية: | convolutional neural network, u-net, segmentation, deep learning, land use, banana plantation, panama tr4, aerial photography, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999 |
| الوصف: | Bananas are the world’s most popular fruit and an important staple food source. Recent outbreaks of Panama TR4 disease are threatening the global banana industry, which is worth an estimated $8 billion. Current methods to map land uses are time- and resource-intensive and result in delays in the timely release of data. We have used existing land use mapping to train a U-Net neural network to detect banana plantations in the Wet Tropics of Queensland, Australia, using high-resolution aerial photography. Accuracy assessments, based on a stratified random sample of points, revealed the classification achieves a user’s accuracy of 98% and a producer’s accuracy of 96%. This is more accurate compared to existing (manual) methods, which achieved a user’s and producer’s accuracy of 86% and 92% respectively. Using a neural network is substantially more efficient than manual methods and can inform a more rapid respond to existing and new biosecurity threats. The method is robust and repeatable and has potential for mapping other commodities and land uses which is the focus of future work. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-3417 10062017 |
| Relation: | https://www.mdpi.com/2076-3417/10/6/2017; https://doaj.org/toc/2076-3417 |
| DOI: | 10.3390/app10062017 |
| URL الوصول: | https://doaj.org/article/4d4781b3f3bd4632a598b7be4c7b72a2 |
| رقم الأكسشن: | edsdoj.4d4781b3f3bd4632a598b7be4c7b72a2 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20763417 10062017 |
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| DOI: | 10.3390/app10062017 |